Netherlands

Disparate impact rules are formally neutral but indirectly discriminate against protected groups (i.e., by targeting a characteristic that is more prevalent in a given group). Because these rules are not obviously malicious, they have been widely enacted to circumvent policies against explicit discrimination. In a series of four experiments, we show that adults and children are sensitive to the moral implications of disparate impact rules. However, we also find that they are more accepting of these rules when strong justification is provided, compared to rules with no justification. Crucially, demographic differences also impact people&#39;s judgments of disparate impact rules and their creators. We find that conservatives and those from groups not directly affected by the rule tend to be more accepting of it. By studying people&#39;s reasoning about disparate impact rules, this work aims to identify the mechanisms by which these rules may evade detection. Finally, we discuss how these insights may inform the development of interventions that highlight the problematic effects of indirectly discriminatory policies.

**Authors:**

Aarthi Popat: Yale University; Jamie Amemiya: Occidental College; Gail D. Heyman: UC San Diego; Caren M. Walker: University of California San Diego

CogSci 2024

The Hair Club for Boys: How children and adults judge disparate impact rules

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ACL-IJCNLP 2021

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP).

Disparate impact rules are formally neutral but indirectly discriminate against protected groups (i.e., by targeting a characteristic that is more prevalent in a given group). Because these rules are not obviously malicious, they have been widely enacted to circumvent policies against explicit discrimination. In a series of four experiments, we show that adults and children are sensitive to the moral implications of disparate impact rules. However, we also find that they are more accepting of these rules when strong justification is provided, compared to rules with no justification. Crucially, demographic differences also impact people's judgments of disparate impact rules and their creators. We find that conservatives and those from groups not directly affected by the rule tend to be more accepting of it. By studying people's reasoning about disparate impact rules, this work aims to identify the mechanisms by which these rules may evade detection. Finally, we discuss how these insights may inform the development of interventions that highlight the problematic effects of indirectly discriminatory policies.

**Authors:**

Aarthi Popat: Yale University; Jamie Amemiya: Occidental College; Gail D. Heyman: UC San Diego; Caren M. Walker: University of California San Diego

technical paper

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Welcome to the 2024 Meeting of the Cognitive Science Society in Rotterdam! CogSci 2024 brings together a large community of cognitive scientists who have traveled here from around the world, as well as a smaller group of remote presenters. I would like to extend an especially warm welcome to our first-time CogSci meeting attendees, and hope that 
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The Cognitive Science Society relies on the volunteer efforts of our Governing Board members who work on matters related to our membership; conference policies; diversity and inclusion, international, and outreach initiatives; prizes and much more. We welcome interest from Society members who would like to become more involved in what we do. The 
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I hope you enjoy this year’s meeting and the many different opportunities to engage with the vibrant community of cognitive scientists gathered here. I also hope you get a chance to explore the great city of Rotterdam with its many attractions, museums, restaurants and scenic views. If you’d like to talk more about our Society, or simply want to say hello, please stop by!

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**Anna Papafragou** 
President, Cognitive Science 
Society 2023-2024

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The 46th Annual Meeting of the Cognitive Science Society presents the latest research across cognitive science and highlights the theme of Cognition in Context.

Spatial demonstratives are deictic expressions used to point to a referent with language. In the standard view, they encode a spatial proximal\distal contrast between “near” (this) and “far” (that) from the speaker. Several studies have shown that such contrast maps on a perceptual contrast between peripersonal and extrapersonal space. Still, other factors beyond spatial distance influence demonstrative choice. Here we investigate whether the proximal/distal contrast maps also onto a more general contrast between being in physical control/not in control of a target referent. Participants were presented with two circles (red and blue) on a screen. They had to move them with the mouse to find the target circle (the one with two gaps). One circle followed the mouse trajectory (controllable), while the other moved randomly in the center of the screen (not controllable). Unknown to the participants, the gaps only appeared if the stimuli crossed a distance threshold. Importantly, participants had to use stimulus controllability to solve the task. They were instructed to answer by indicating the target to the experimenter using this/that and red/blue (in Italian questo/quello and rosso/blu). Results show that participants used the proximal demonstrative more frequently to refer to the target stimulus when in control. These findings suggest that, similarly to spatial distance, physical control influences demonstrative choice.

**Authors:**

Angelo Mattia Gervasi: Sapienza University of Rome; Anna Borghi: Sapienza University of Rome; Francesco Mannella: National Research Council; Luca Tummolini: National Research Council of Italy

Spatial demonstratives and physical control

Toddlers can only say one or two words at a time. What do they choose to talk about? We report preliminary results (N=167; mean; 19.5 months) from a pre-registered online experiment on productive language. Toddlers saw six movies. A curtain opened on an introductory scene, the parent closed their eyes, and a new event happened. The curtain closed and the child was asked what happened. On two trials the unseen event was new to the parent (Novel event); on two trials, one of two animals ate the only food in the scene (Agent ambiguous); on two trials, the only animal ate one of the two foods (Patient ambiguous). We predicted that toddlers would selectively generate informative utterances (i.e., referring to the novel event, the agent, and the patient, respectively). Toddlers' productive language was indeed sensitive to what listeners’ know; however, unlike adults, they selectively referred to information in common ground.

**Authors:**

Bradli Washington: Massachusetts Institute of Technology; Sienna Radifera: Massachusetts Institute of Technology; Melissa Kline Struhl: Massachusetts Institute of Technology; Laura Schulz: Massachusetts Institute of Technology

From the mouths of babes: Toddlers’ early word production favors information in common ground

In this paper we investigate whether subject doubling in French is affected by the Uniform Information Density (UID) principle, which states that speakers prefer language encoding that minimizes fluctuations in information density. We show that, other factors being controlled, speakers are more likely to double the NP subject when it has a high surprisal, thus providing further empirical evidence to the UID principle which predicts a surprisal-redundancy trade-off as a property of natural languages. We argue for the importance of employing GPT-2 to investigate complex linguistic phenomena such as subject doubling, as it enables the estimation of subject surprisal by considering a rather large conversational context, a task made possible by powerful language models that incorporate linguistic knowledge through pre-training on extensive datasets.

**Authors:**

Yiming Liang: Universiteit Gent; Pascal Amsili: Université Sorbonne Nouvelle; Heather Burnett: CNRS; Vera Demberg: Saarland University

Uniform information density explains subject doubling in French

This study investigates whether homophone competitors are activated during typewriting and to which extent such activation is modulated by syntactic category. In two experiments, we compared the typewriting of homophone pairs in high vs. low conflict sentences (i.e., both homophones vs. only one homophone in the sentence, respectively) in a sentence dictation task (Experiment 1) and in a question-answering task (Experiment 2). The homophone pairs either belonged to the same or different syntactic categories. In Experiment 1, we found a homophone interference effect in accuracy, independent of conflict and syntactic category. In Experiment 2, this effect was replicated, but in addition, participants were slower to type homophones in a high vs. a low conflict context. Our results show a robust, lexically-situated homophone interference effect, regardless of conflict and syntactic category, but when deeper processing of the sentence is involved, conflict starts to play a role.

**Authors:**

Merel Muylle: Ghent University; Nazbanou Nozari: Indiana University; Robert Hartsuiker: Ghent University

On idle idols and ugly icons: Do homophones create interference in typing?

Autoregressive Large Language Models (LLMs) trained for next-word prediction have demonstrated remarkable proficiency at producing coherent text. But are they equally adept at forming coherent probability judgments? We use probabilistic identities and repeated judgments to assess the coherence of probability judgments made by LLMs. Our results show that the judgments produced by these models are often incoherent, displaying human-like systematic deviations from the rules of probability theory. Moreover, when prompted to judge the same event, the mean-variance relationship of probability judgments produced by LLMs shows an inverted-U-shaped like that seen in humans. We propose that these deviations from rationality can be explained by linking autoregressive LLMs to implicit Bayesian inference and drawing parallels with the Bayesian Sampler model of human probability judgments.

**Authors:**

Jian-Qiao Zhu: Princeton University; Tom Griffiths: Princeton University 

Incoherent Probability Judgments in Large Language Models

Humans extract useful abstractions of the world from noisy sensory data. Serial reproduction allows us to study how people construe the world through a paradigm similar to the game of telephone, where one person observes a stimulus and reproduces it for the next to form a chain of reproductions. Past serial reproduction experiments typically employ a single sensory modality, but humans often communicate abstractions of the world to each other through language. To investigate the effect language on the formation of abstractions, we implement a novel multimodal serial reproduction framework by asking people who receive a visual stimulus to reproduce it in a linguistic format, and vice versa. We ran unimodal and multimodal chains with both humans and GPT-4 and find that adding language as a modality has a larger effect on human reproductions than GPT-4's. This suggests human visual and linguistic representations are more dissociable than those of GPT-4.

**Authors:**

Sreejan Kumar: Princeton University; Raja Marjieh: Princeton University; Byron Zhang: Princeton University; Declan Campbell: Princeton University; MICHAEL Y HU: New York University; Umang Bhatt: New York University; Brenden Lake: NYU; Tom Griffiths: Princeton University 

Comparing Abstraction in Humans and Machines Using Multimodal Serial Reproduction

We investigate whether more accurate representation of syntactic
information in Transformer-based language models is
associated with better alignment to brain activity. We use fMRI
recordings from a large dataset (MOUS) of a Dutch sentence
reading task, and perform Representational Similarity Analysis
to measure alignment with 2 mono- and 3 multilingual
language models. We focus on activity in a region known
for syntactic processing (the Left posterior Medial Temporal
Gyrus). We correlate model-brain similarity scores with the
accuracy of dependency structures extracted from model internal
states using a labelled structural probe. We report three key
findings: 1) Accuracy of syntactic dependency representations
correlates with brain similarity, 2) The link between brain similarity
and dependency accuracy persists regardless of sentence
complexity, although 3) Sentence complexity decreases dependency
accuracy while increasing brain similarity. These results
highlight how interpretable, linguistic features such as syntactic
dependencies can mediate the similarity between language
models and brains

**Authors:**

Abraham Jacob Fresen: University of Amsterdam; Rochelle Choenni: ILLC; Micha Heilbron: University of Amsterdam; Willem Zuidema: University of Amsterdam; Marianne de Heer Kloots: Institute for Logic, Language and Computation

Language Models That Accurately Represent Syntactic Structure Exhibit Higher Representational Similarity To Brain Activity

Large scale neural models show impressive performance across a wide array of linguistic tasks. Despite this they remain, largely, black-boxes - learning vector-representations of their input that prove difficult to interpret. This limits our ability to understand what they learn, and when the learn it, or characterise why they often fail to generalise systematically. To address this we introduce a novel approach to interpretability that looks at the mapping a model learns from sentences to representations as a kind of language in its own right. In doing so we introduce a set of information-theoretic measures that quantify how structured a model's representations are with respect to its input, and when during training that structure arises. Our measures are fast to compute, grounded in linguistic theory, and can predict which models will generalise best based on their representations. We use these measures to describe two distinct phases of training a transformer: an initial phase of in-distribution learning which reduces task loss, then a second stage where representations becoming robust to noise. Generalisation performance begins to increase during this second phase, drawing a link between generalisation and robustness to noise. Finally we look at how model size affects the structure of the representational space, showing that larger models ultimately compress their representations more than their smaller counterparts.

**Authors:**

Henry Conklin: University of Edinburgh; Kenny Smith: University of Edinburgh

Deep Learning as Language Emergence: Noise Drives Generalisation

Cognitive control is thought to regulate the conflict between stability---maintaining the current task in the face of distraction---and flexibility---switching to a new task of greater priority. However, evidence conflicts regarding when and to what extent stability and flexibility trade-off. A normative theory of flexibility and stability may help clarify when and why we should expect such trade-offs to occur. Towards such a theory, we model task-switching as a problem of decision-making under uncertainty, in which the decision-maker must simultaneously infer both the identity of a stimulus and the task governing the correct response to that stimulus. We find that optimal behavior is either extremely stable or extremely flexible, but not both, indicating a normative basis for a trade-off between the two. However, we also show that a sub-optimal but more realistic decision-maker exhibits behavior between these two extremes, and more closely resembles experimental data.

**Authors:**

Seth Madlon-Kay: Duke University; John Pearson: Duke University; Tobias Egner: Duke University

Optimal decision-making under task uncertainty: a computational basis for cognitive stability versus flexibility

Our choices often require us to prioritize some features of our rich sensory experience over others. Past work suggests that humans solve this problem by focusing on relevant information while discarding that which is irrelevant. Yet learning which features to prioritize requires extensive experience. Moreover, features that are irrelevant now may become relevant in the future. One way to address these issues is by sampling individual richly encoded experiences from episodic memory. Here we hypothesize that episodic memory is used to guide decisions based on multiple features of past events. We test this hypothesis using an experiment in which participants made choices about the value of features that were present in multiple past experiences. We find evidence suggesting that participants used episodic memories to flexibly access features of past events during decision making. Overall, these results suggest that episodic memory promotes adaptive decisions when knowledge of multiple features is necessary.

**Authors:**

Jonathan Nicholas: New York University; Marcelo G Mattar: New York University

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